PT  - JOURNAL ARTICLE
AU  - Philipp Schwartenbeck
AU  - Karl Friston
TI  - Computational Phenotyping in Psychiatry: A Worked Example
AID  - 10.1523/ENEURO.0049-16.2016
DP  - 2016 Jul 01
TA  - eneuro
PG  - ENEURO.0049-16.2016
VI  - 3
IP  - 4
4099  - http://www.eneuro.org/content/3/4/ENEURO.0049-16.2016.short
4100  - http://www.eneuro.org/content/3/4/ENEURO.0049-16.2016.full
SO  - eneuro2016 Jul 01; 3
AB  - Computational psychiatry is a rapidly emerging field that uses model-based quantities to infer the behavioral and neuronal abnormalities that underlie psychopathology. If successful, this approach promises key insights into (pathological) brain function as well as a more mechanistic and quantitative approach to psychiatric nosology—structuring therapeutic interventions and predicting response and relapse. The basic procedure in computational psychiatry is to build a computational model that formalizes a behavioral or neuronal process. Measured behavioral (or neuronal) responses are then used to infer the model parameters of a single subject or a group of subjects. Here, we provide an illustrative overview over this process, starting from the modeling of choice behavior in a specific task, simulating data, and then inverting that model to estimate group effects. Finally, we illustrate cross-validation to assess whether between-subject variables (e.g., diagnosis) can be recovered successfully. Our worked example uses a simple two-step maze task and a model of choice behavior based on (active) inference and Markov decision processes. The procedural steps and routines we illustrate are not restricted to a specific field of research or particular computational model but can, in principle, be applied in many domains of computational psychiatry.